reflow VS ballista

Reflow [1] is a similar attempt at a slightly different domain: bioinformatics and ETL pipelines. Reflow exposes a data model and programming model that reclaims programmability in these systems, and, by leaning on these abstractions, gives the runtime much more leeway to do interesting things. It unties the hands of the implementer.

[1] https://github.com/grailbio/reflow

https://github.com/grailbio/reflow is the closest that I know, as it has a design that resembles the Bazel build system.

It seems Reflow falls in this category:

https://github.com/grailbio/reflow

> Reflow thus allows scientists and engineers to write straightforward programs and then have them transparently executed in a cloud environment. Programs are automatically parallelized and distributed across multiple machines, and redundant computations (even across runs and users) are eliminated by its memoization cache. Reflow evaluates its programs incrementally: whenever the input data or program changes, only those outputs that depend on the changed data or code are recomputed.

His newer project, Ballista, was also donated to Apache Arrow. I hope to get the Rust skills to collaborate with him on open source work someday too. He's also doing really cool work on spark-rapids FYI.

https://github.com/ballista-compute/ballista/blob/main/rust/executor/src/flight_service.rs#L193-L228

I'm guessing Polars and Ballista (https://github.com/ballista-compute/ballista) have different goals, but I don't know enough about either to say what those might be. Does anyone know enough about either to explain the differences?

In your article you mention looking for a faster data engine, have you looked at Ballista https://github.com/ballista-compute/ballista? It’s pretty young but it uses the Apache Arrow memory model and the maintainer did a bunch of work on Apache Spark I believe.

https://github.com/ballista-compute/ballista is also a cool project worth checking out.

It’s mostly a personal favourite, but once Ballista [1] gets a bit more developed, I expect we’ll tear out our Java/Spark pipelines and replace them with that.

The ML ecosystem in Rust is a bit underdeveloped at the moment, but work is ticking along on packages like Linfa and SmartCore, so maybe it’ll get there? In my field I’m mostly about it’s potential for correct, high-performance data pipelines that are straightforward to write in reasonable time, and hopefully a model-serving framework: I hate that so many of the current tools require annotating and shipping Python when really model-serving shouldn’t really need any Python code.

[1] https://github.com/ballista-compute/ballista

I've looked at this and thought it looked amazing, but also haven't used it for anything. Some thoughts...

Rust is a blessing and curse. I seems like the obvious choice for data pipelines, but everything big currently exists in Java and the small stuff is in Javascript, Python or R. Maybe this will slowly change, but it's a big ship to turn. I'm hopeful that tools like this and Balista [1] will eventually get things moving.

Since the Rust community is relatively small, language bindings would be very helpful. Being able to configure pipelines from Java or Typescript(!) would be great.

Or maybe it's just that this form of computation is too foreign. By the time you need it, the project is so large that it's too late to redesign it to use it. I'm also unclear on how it would handle changing requirements and recomputing new aggregations over old data. Better docs with more convincing examples would be helpful here. The GitHub page showing counting isn't very compelling.

[1] https://github.com/ballista-compute/ballista